Workshop on Computer Vision in the Wild
@ ECCV 2022, October 23


State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept.

Recent works show that learning from large-scale image-text data is a promising approach to building transferable visual models that can effortlessly adapt to a wide range of downstream computer vision (CV) and multimodal (MM) tasks. For example, CLIP , ALIGN and Florence for image classification, ViLD , RegionCLIP and GLIP for object detection. These vision models with language interface are naturally open-vocabulary recogntion models, showing superior zero-shot and few-shot adaption performance on various real-world scenarios.

We propose this "Computer Vision in the Wild" workshop, aiming to gather academic and industry communities to work on CV problems in real-world scenarios, focusing on the challenge of open-set/domain visual recognition and efficient task-level transfer. Since there is no established benchmarks to measure the progress of "CV in the Wild", we develop new benchmarks for image classification and object detection, to measure the task-level transfer ablity of various models/methods over diverse real-world datasets, in terms of both prediction accuracy and adaption efficiency. This workshop will also host two challenges based on the benchmarks.

Call for Papers

    Topics of interest include but are not limited to:
  • Open-set visual recognition methods, including classification, object detection, segmentation in images and videos
  • Zero/Few-shot text-to-image generation/editing; Open-domain visual QA & image captioning
  • Unified neural networks architectures and training objectives over different CV & MM tasks
  • Large-scale pre-training, with images/videos only, image/video-text pairs, and external knoweldge
  • Efficient large visual model adaptation methods, measured by #training samples (zero-shot and few-shot), #trainable parameter, throughput, training cost
  • New metrics / benchmarks / datasets to evaluate task-level transfer and open-domain visual recognition

  • We accept abstract submissions to our workshop. All submissions shall have maximally 8 pages (excluding references) following the ECCV 2022 author guidelines. All submissions will be reviewed by the Program Committee on the basis of technical quality, relevance to scope of the conference, originality, significance, and clarity.

    Submission Portal: [CMT]

CV in the Wild Challenges

    There are two challenges associated with this workshop: "Image Classification in the Wild" (ICinW) and "Object Detection in the Wild" (ODinW). We summarize their evaluation datasets and metrics in the table below.

    Eval Datasets
    Eval Metrics
    Make a Submission
    20 Image Classification Datasets
    Zero, few, full-shot
    35 Object Detection Datasets
    Zero, few, full-shot
    Open Soon
    To prevent a race purely in pre-training data and model size, we will have two tracks.
  • For the academic track, pre-training data is limited to ImageNet21k, Objects365, CC15M, and YFCC15M
  • For the industry track, there is no limitation on pre-training data and model size. Teams are required to disclose meta info of model and data if extra data is used.

  • More information about the challenges are released: [Benchmark] [Document] . Our evaluation server will be online soon.


July 25, 2022 Competition starts, testing phase begins
September 30, 2022 Competition ends (challenge paper submission)
September 16, 2022 Workshop paper submission deadline
October 9,2022 Workshop paper acceptance decision to authors
October 16,2022 Camera-ready submission deadline

Invited Speakers (TBD)

Program (TBD)

Workshop Organizers

Pengchuan Zhang
Meta AI

Chunyuan Li

Jyoti Aneja

Ping Jin

Jianwei Yang

Xin Wang

Haotian Liu
UW Madison

Liunian Li

Haotian Zhang
University of Washington

Shohei Ono

Challenge Organizers (TBD)

Yinfei Yang

Yi-Ting Chen

Ye Xia

Yangguang Li

Feng Liang
UT Austin

Yufeng Cui

Kunaiki Saito

Kihyuk Sohn

Xiang Zhang

Chun-Liang Li

Chen-Yu Lee

Houwen Peng

Advisory Committee

Trevor Darrell
UC Berkley

Lei Zhang

Jenq-Neng Hwang
University of Washington

Yong Jae Lee
UW Madison

Houdong Hu

Zicheng Liu

Ce Liu

Xuedong Huang

Kai-Wei Chang

Jingdong Wang

Zhuowen Tu

Jianfeng Gao

Workshop and Challenge Questions?
Reach out:
Workshop Organizing Team